Prediction of hydrate equilibrium conditions using k-nearest neighbor algorithm to CO2 capture

Gas or clathrate hydrates are an important issue when they form in the oil and gas pipelines. Since the determination of the hydrate formation temperature and pressure is very difficult experimentally for every gas system and it is impossible in terms of cost and time approximately, mathematical models can be useful tools to overcome these difficulties. In this study, k-nearest neighbor model was used to predict the equilibrium conditions of hydrate formation in absorption and separation of carbon dioxide from flue gas mixture, containing carbon dioxide and nitrogen. At the training phase, temperature and composition data of nitrogen and carbon dioxide in the flue gas mixture at equilibrium conditions and the equilibrium pressures of hydrate formation were used as input and output, respectively. The error percentage less than 0.38% indicates the high accuracy of the proposed model. In this study, 80%, 85%, and 90% of the training data are examined for three numbers of nearest. For three numbers of used nearest (k = 1, k = 2 and k = 3), the value of k = 1 leads to the lowest error; so, it is selected as the best nearest in the presented model.     

Hydrolytic cleavage of ammonia-borane complex for hydrogen production

A new process for generating hydrogen via near room temperature hydrolysis of AB complex using small amounts of platinum group metal catalyst has been studied. Using in situ ¹¹B NMR spectroscopy, the overall rate of K₂Cl₆Pt catalyzed hydrolysis of AB complex was calculated to be third-order. The pre-exponential factor (A) and the activation energy (E_a) of Arrhenius equation, ln k = ln A − E_a/RT, were determined to be: A = 1.6 × 10¹¹ L mol⁻¹ s⁻¹ and E_a = 86.6 kJ mol⁻¹ for temperature range of (25–35 °C). X-ray photoelectron spectroscopy of the residue suggested that the platinum salt was reduced from Pt⁴⁺ to Pt⁰ within the course of the reaction and X-ray diffraction analysis pattern for the residue showed crystallized single-phase boric acid.     

Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection.

A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.     

DFNS/PEI/Cu Nanocatalyst for Reduction of Nitro-aromatic Compounds

Catalysis Letters - Nitro-aromatic pollution in industrial waste streams threat wellbeing of water resources. This study investigates the performance of a copper-based nano catalyst to reduce...     

Synergistic Use of Pyridine and Selenophene in a Diketopyrrolopyrrole‐Based Conjugated Polymer Enhances the Electron Mobility in Organic Transistors

To achieve semiconducting materials with high electron mobility in organic field‐effect transistors (OFETs), low‐lying energy levels (the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)) and favorable molecular packing and ordering are two crucial factors. Here, it is reported that the incorporation of pyridine and selenophene into the backbone of a diketopyrrolopyrrole (DPP)‐based copolymer produces a high‐electron‐mobility semiconductor, PDPPy‐Se. Compared with analogous polymers based on other DPP derivatives and selenophene, PDPPy‐Se features a lower LUMO that can decrease the electron transfer barrier for more effective electron injection, and simultaneously a lower HOMO that, however, can increase the hole transfer barrier to suppress the hole injection. Combined with thermal annealing at 240 °C for thin film morphology optimization to achieve large‐scale crystallite domains with tight molecular packing for effective charge transport along the conducting channel, OFET devices fabricated with PDPPy‐Se exhibit an n‐type‐dominant performance with an electron mobility (μ_e) as high as 2.22 cm² V^?1 s^?1 and a hole/electron mobility ratio (μ_h/μ_e) of 0.26. Overall, this study demonstrates a simple yet effective approach to boost the electron mobility in organic transistors by synergistic use of pyridine and selenophene in the backbone of a DPP‐based copolymer.     

Electrocatalytic activity of Pt nanoparticles supported on novel functionalized reduced graphene oxide-chitosan for methanol electrooxidation

Here, graphene oxide (GO) was synthesized by a modified Hummers’ method and was functionalized with 1,1′-dimethyl-4,4′-bipyridinium dichloride (MV) accompanied by chitosan (CH) to prepare a novel MV-RGO-CH support. Pt/MV-RGO-CH catalyst was prepared by immobilization of the Pt nanoparticles on MV-RGO-CH support. The microstructure and morphology of the prepared catalyst was characterized by transmission electron microscopy and X-ray powder diffraction analysis. The electrocatalytic activity of Pt/MV-RGO-CH catalyst was investigated for methanol electrooxidation through cyclic voltammetry (CV), CO_ads stripping voltammetry, chronoamperometry, and electrochemical impedance spectroscopy (EIS) techniques. The effects of some experimental factors for methanol electrooxidation such as methanol concentration, scan rate and temperature were studied at the prepared catalyst. Durability of the catalyst was also investigated. Comparing the catalytic activity of the Pt/MV-RGO-CH nanocatalyst with Pt/CH and Pt/MV-RGO catalysts indicated that Pt/MV-RGO-CH has a very good catalytic activity for methanol electrooxidation.     

Impacts of Paris agreement,fossil fuel consumption,and net energy imports on CO2 emissions: a panel data approach for three West European countries

Clean Technologies and Environmental Policy - The Paris agreement is a legally binding international treaty on climate change. This study examines the Paris agreement and other explanatory...     

Determination of phenobarbital in real sample using carbon quantum dots modified with tungsten as a fluorescent nanoprobe

Journal of Materials Science: Materials in Electronics - In this study, a sensitive and selective fluorescence nanoprobe has been designed based on carbon quantum dots modified by tungsten (WCQDs)...     

A self‐adaptive algorithm for super‐peer selection considering the mobility of peers in cognitive mobile peer‐to‐peer networks

Cognitive peer‐to‐peer networks are obtained from a combination of cognitive networking concepts and peer‐to‐peer networks. These networks are able to improve their performance while operating under dynamic and unknown environments. A cognitive peer‐to‐peer network tries to learn an appropriate configuration for itself considering the unknown physical properties of peers. Cognitive mobile peer‐to‐peer networks refer to cognitive peer‐to‐peer networks which are built over mobile ad hoc networks. In these networks, heterogeneity of the mobility of peers and resource limitation in wireless networks create challenges for network management algorithms. Because of the dynamicity of these networks, the management algorithms should be designated in self‐adaptive manner. In one type of these networks, some peers, called super‐peers, undertake to perform network managerial tasks. The mobility of peers leads to connection failure among peers and reselection of new super‐peers. Therefore, the selection of super‐peers, due to their influential role, requires an algorithm that considers the peers' mobility. Up to now, no self‐adaptive algorithm has been designated for super‐peer selection considering the mobility of peers in a self‐adaptive manner. This paper proposes M‐SSBLA, a self‐adaptive algorithm for super‐peer selection considering the mobility of peers based on learning automata. The proposed algorithm is obtained from cooperation between a learning automata‐based cognitive engine and MIS. MIS is a well‐known super‐peer selection algorithm in mobile peer‐to‐peer networks. We compared the proposed algorithm with recently reported algorithms, especially for a network with high mobility. Simulation results show that the proposed algorithm can cover maximum ordinary‐peer with a few super‐peer and improve robustness against super‐peer failures while decreasing maintenance overhead.